There's a growing need to analyze major policies using data that reflects
changes in urban conditions and the environment. To efficiently analyze the
demand for urban management policies and promote them, generating
appropriate indicators through data analysis is crucial. We are considering
actively utilizing big data (such as mobile communication and card company
sales data) and administrative data (like permits) to compensate for the
limitations of existing statistical data (e.g., timeliness and measurement
accuracy). This will help derive empirical indicators that more accurately reflect
real-life situations. Data-driven analysis is expected to enhance reliability by
efficiently analyzing and utilizing diverse and extensive data compared to
existing techniques.
It's essential to analyze and utilize converged data that aligns with policy
goals and relevance. This involves data collection, combining data with high
policy applicability, identifying convergent data, and establishing standard
analysis guidelines. Some of these data types include population (including
number, movement, density), transportation (traffic congestion, public
transportation usage), housing transactions and prices, local industries, and
energy-related data. These can offer valuable insights for policy activities and
data analysis. Data-driven urban management indicators can provide alternative
approaches in planning spatial and living areas, deploying urban infrastructure,
and formulating transportation policies to address urban traffic issues. These
analytics can be applied in various fields, not just urban management, but also
in shaping the future of cities.
Urban management data should consider ease of acquisition, data quality,
and granular analysis. Data-driven urban management indicators should not
only focus on data collection and quality but also on creating indicators
applicable to policy and region-specific detailed analysis. Unlike typical
indicators, the collection and integrity of raw data are critical, and the data
collection scheme should be carefully considered. Additionally, it's essential to
assess the appropriate spatial resolution for each data type, as urban problems
may vary from region to region.
Multiscale analysis allows for tailored policies based on inter- and
intra-regional disparities. To effectively incorporate spatial scale into policy,
region-specific analytical metrics are necessary. These metrics will facilitate
customized policy development based on regional disparities.
To support city management, establish a common integrated data collection
and management system. Developing necessary indicators for city management
and monitoring requires an ongoing support system and institutional framework
for data collection, analysis, and management. Promote and manage these
efforts at the metropolitan level. For integrated collection and management
of public data, provide standard guidelines for verifying private data consistency
and processing them through data analysis and research.
Research and analysis are also needed to develop new policy indicators using
private and administrative data. Quasi-public organizations like researchers play
an essential role in ensuring data validity and accuracy, contributing to the
development of new management methods and policies.
Establish a policy platform and governance operation system for data
utilization. First, create a data management platform to collect and manage
data required for city management in an integrated manner. This platform can
offer policy indicators through dashboards for citizens and businesses, serving
as a policy formulation and work support platform. Additionally, build an
infrastructure for developing various analysis models using convergence
indicators and operate an open analysis platform to provide information, share
analysis results, support education, and facilitate competitions to address various
city issues.
When considering data utilization, establish a governance system for
horizontal and vertical utilization. This governance system plays a crucial role
in effectively using big data to support the sustainable development of cities,
enhance urban infrastructure and services, and improve the quality of life.